app.py

import torch
from diffusers import StableDiffusion3Pipeline
from huggingface_hub import login
import os
import gradio as gr

# Retrieve the token from the environment variable
token = os.getenv("HF_TOKEN")  # Hugging Face token from the secret
if token:
    login(token=token)  # Log in with the retrieved token
else:
    raise ValueError("Hugging Face token not found. Please set it as a repository secret in the Space settings.")

# Load the Stable Diffusion 3.5 model with lower precision (float16)
model_id = "stabilityai/stable-diffusion-3.5-large"
pipe = StableDiffusion3Pipeline.from_pretrained(model_id, torch_dtype=torch.float16)  # Use float16 precision
pipe.to("cpu")  # Ensuring it runs on CPU

# Define the path to the LoRA model
lora_model_path = "./lora_model.pth"  # Assuming the file is saved locally

# Custom method to load and apply LoRA weights to the Stable Diffusion pipeline
def load_lora_model(pipe, lora_model_path):
    # Load the LoRA weights
    lora_weights = torch.load(lora_model_path, map_location="cpu")
    
    # Apply weights to the UNet submodule
    for name, param in pipe.unet.named_parameters():  # Accessing unet parameters
        if name in lora_weights:
            param.data += lora_weights[name]

    return pipe

# Load and apply the LoRA model weights
pipe = load_lora_model(pipe, lora_model_path)

# Function to generate an image from a text prompt
def generate_image(prompt, seed=None):
    generator = torch.manual_seed(seed) if seed is not None else None
    # Reduce image size for less memory usage
    image = pipe(prompt, height=512, width=512, generator=generator).images[0]  # Changed image size
    return image

# Gradio interface
iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Enter your prompt"),  # For the prompt
        gr.Number(label="Enter a seed (optional)", value=None),  # For the seed
    ],
    outputs="image"
)
iface.launch()